SMALL AREA HOUSING DEFICIT ESTIMATION: A SPATIAL MICROSIMULATION APPROACH

Abstract

This paper presents a new methodology for measuring housing deficit at small areas. It combines the advantages of two types of census data: (a) individual-level sample data, which are very useful for depicting many dimensions of the housing deficit, but do not present detailed geographic information; and (b) universal data with detailed spatial resolution (census tracts), but aggregated. For that, we explore an approach based on spatial microsimulation. We simulate spatial microdata by using aggregate data as constraints to expand and allocate individual-level data to census tracts. This procedure allowed us to estimate a particular dimension of the housing deficit (housing cost) at higher spatial resolution.